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800 Years of Research at Padova University

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A special issue of Sensors (ISSN 1424-8220).

Deadline for manuscript submissions: closed (31 December 2022) | Viewed by 53629

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Prof. Dr. Alessandro Martucci

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Guest Editor

Department of Industrial Engineering, University of Padua, Padua, Italy
Interests: optical gas sensors; plasmonic gas sensors; sol-gel thin films; nanocomposites; nanoparticles; photonic materials and devices

Prof. Dr. Giovanni Sparacino

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Guest Editor

Department of Information Engineering, University of Padova, Padova, Italy
Interests: sensors and algorithms for continuous glucose monitoring; deconvolution and parameter estimation techniques for the study of physiological systems; linear and nonlinear biological time-series analysis; measurement and processing of biomedical signals (EEG, event-related potentials, local field potentials, fNIRS, etc.) for clinical research and applications
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Lorenzo Vangelista

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Guest Editor

Department of Information Engineering, University of Padova, Via Gradenigo 6/B, 35131 Padova, Italy
Interests: low power wide area networks (including LoraWAN, NB-IoT, etc.); wireless sensors and actuators networks; Internet of Things; smart cities; telecommunications; signal processing; modulation and demodulation; 5G enabling technologies; broker/messaging platforms (including MQTT, etc.)
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Dating back to 1222, the University of Padova (UNIPD) is one of the leading Universities in Italy and has a long tradition of scientific excellence. UNIPD offers its students 32 departments, 37 doctoral degree courses, and 44 research and service centers across the spectrum of sciences, medicine, social sciences, and humanities, with about 2300 professors and researchers employed. Nearly 60 UNIPD spinoffs are presently active. UNIPD currently participates in more than 240 EU-funded Horizon 2020 actions, with a total budget of 78 Million Euro, as well as in 20 projects in other EU programs, with a budget of around 2 Million Euro. Within the previous 7th Framework Program, it participated in 196 European research projects accounting for more than 70 Million Euro. UNIPD coordinated 40 FP7 projects and it is now coordinating 50 H2020 projects.

This Special Issue aims to celebrate the 800 years of interdisciplinary research at UNIPD. Contributions to the Special Issue are solicited from people currently engaged in scientific research at UNIPD, as well as anyone participating in collaborations with UNIPD. Papers from distinguished alumni of UNIPD are also very welcome.

This Special Issue's scope is to show and reinforce the distinctive characteristic of the 800 years of history of UNIPD, i.e., its multidisciplinarity. The topic of "sensors" fits the aim, as sensors are used in so many fields, ranging from medicine to physics, from engineering to psychology and humanities. We seek contributions from (but not limited to) the following topics:

Sensors for cultural heritage preservation
Sensor for music and sound restoration, preservation, and processing
Sensors in healthcare and human–machine interaction
Sensors in cognitive psychology and neuroscience
Physical and chemical sensors, and biosensors
Smart/intelligent sensors and related algorithms
Sensor devices, principles, technology, and application
Optomechanical, optoelectronic, and photonic sensors
Sensor arrays, chemometrics, and micro- and nano-sensors
Signal processing, data fusion and deep learning in sensor systems
Advanced materials for sensing
Localization and object tracking
Sensing and imaging, image sensors, vision-/camera-/radar-based sensors, and 3D sensing
Action recognition
Sensors historical evolution in technology and science
Machine/deep learning and artificial intelligence in sensing and imaging
Communications and signal processing for sensing systems
Wearable sensors, devices and electronics, and IoT applications

Prof. Dr. Alessandro Martucci
Prof. Dr. Giovanni Sparacino
Prof. Dr. Lorenzo Vangelista
Guest Editors

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

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Published Papers (13 papers)

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Research

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11 pages, 2135 KiB

Open AccessArticle

Optical Hydrogen Sensing Properties of e-Beam WO₃ Films Decorated with Gold Nanoparticles

by Elena Colusso, Michele Rigon, Alain Jody Corso, Maria Guglielmina Pelizzo and Alessandro Martucci

Sensors 2023, 23(4), 1936; https://doi.org/10.3390/s23041936 - 9 Feb 2023

Cited by 1 | Viewed by 1955

Abstract

Tungsten oxide thin films with different thicknesses, crystallinity and morphology were synthesized by e-beam deposition followed by thermal treatment and acid boiling. The films with different surface morphologies were coated with gold nanoparticles and tested as optical sensing materials towards hydrogen. X-ray diffraction, scanning electron microscopy, ellipsometry and UV-VIS spectroscopy were employed to characterize the structural, morphological and optical properties of the film. We demonstrated a good response towards hydrogen in air, reaching a good selectivity among other common reducing gases, such as ammonia and carbon monoxide. The sensitivity has been proven to be highly dependent on the thickness and crystallinity of the samples. Full article

(This article belongs to the Special Issue 800 Years of Research at Padova University)

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9 pages, 622 KiB

Open AccessCommunication

Mobility Classification of LoRaWAN Nodes Using Machine Learning at Network Level

by Lorenzo Vangelista, Ivano Calabrese and Alessandro Cattapan

Sensors 2023, 23(4), 1806; https://doi.org/10.3390/s23041806 - 6 Feb 2023

Cited by 4 | Viewed by 2130

Abstract

LoRaWAN networks rely heavily on the adaptive data rate algorithm to achieve good link reliability and to support the required density of end devices. However, to be effective the adaptive data rate algorithm needs to be tuned according to the level of mobility of each end device. For that purpose, different adaptive data rate algorithms have been developed for the different levels of mobility of end devices, e.g., for static or mobile end devices. In this paper, we describe and evaluate a new and effective method for determining the level of mobility of end devices based on machine learning techniques and specifically on the support vector machine supervised learning method. The proposed method does not rely on the location capability of LoRaWAN networks; instead, it relies only on data always available at the LoRaWAN network server. Moreover, the performance of this method in a real LoRaWAN network is assessed; the results give clear evidence of the effectiveness and reliability of the proposed machine learning approach. Full article

(This article belongs to the Special Issue 800 Years of Research at Padova University)

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17 pages, 665 KiB

Open AccessArticle

Authenticated Timing Protocol Based on Galileo ACAS

by Francesco Ardizzon, Laura Crosara, Nicola Laurenti, Stefano Tomasin and Nicola Montini

Sensors 2022, 22(16), 6298; https://doi.org/10.3390/s22166298 - 21 Aug 2022

Cited by 7 | Viewed by 2862

Abstract

Global navigation satellite systems (GNSSs) provide accurate positioning and timing services in a large gamut of sectors, including financial institutions, Industry 4.0, and Internet of things (IoT). Any industrial system involving multiple devices interacting and/or coordinating their functionalities needs accurate, dependable, and trustworthy time synchronization, which can be obtained by using authenticated GNSS signals. However, GNSS vulnerabilities to time-spoofing attacks may cause security issues for their applications. Galileo is currently developing new services aimed at providing increased security and robustness against attacks, such as the open service navigation message authentication (OS-NMA) and commercial authentication service (CAS). In this paper, we propose a robust and secure timing protocol that is independent of external time sources, and solely relies on assisted commercial authentication service (ACAS) and OS-NMA features. We analyze the performance of the proposed timing protocol and discuss its security level in relation to malicious attacks. Lastly, experimental tests were conducted to validate the proposed protocol. Full article

(This article belongs to the Special Issue 800 Years of Research at Padova University)

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19 pages, 7001 KiB

Open AccessArticle

Indoor Visual-Based Localization System for Multi-Rotor UAVs

by Massimiliano Bertoni, Stefano Michieletto, Roberto Oboe and Giulia Michieletto

Sensors 2022, 22(15), 5798; https://doi.org/10.3390/s22155798 - 3 Aug 2022

Cited by 15 | Viewed by 2501

Abstract

Industry 4.0, smart homes, and the Internet of Things are boosting the employment of autonomous aerial vehicles in indoor environments, where localization is still challenging, especially in the case of close and cluttered areas. In this paper, we propose a Visual Inertial Odometry localization method based on fiducial markers. Our approach enables multi-rotor aerial vehicle navigation in indoor environments and tackles the most challenging aspects of image-based indoor localization. In particular, we focus on a proper and continuous pose estimation, working from take-off to landing, at several different flying altitudes. With this aim, we designed a map of fiducial markers that produces results that are both dense and heterogeneous. Narrowly placed tags lead to minimal information loss during rapid aerial movements while four different classes of marker size provide consistency when the camera zooms in or out according to the vehicle distance from the ground. We have validated our approach by comparing the output of the localization algorithm with the ground-truth information collected through an optoelectronic motion capture system, using two different platforms in different flying conditions. The results show that error mean and standard deviation can remain constantly lower than

0.11 m

, so not degrading when the aerial vehicle increases its altitude and, therefore, strongly improving similar state-of-the-art solutions. Full article

(This article belongs to the Special Issue 800 Years of Research at Padova University)

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39 pages, 26383 KiB

Open AccessArticle

Gesture, Music and Computer: The Centro di Sonologia Computazionale at Padova University, a 50-Year History

by Sergio Canazza, Giovanni De Poli and Alvise Vidolin

Sensors 2022, 22(9), 3465; https://doi.org/10.3390/s22093465 - 2 May 2022

Cited by 11 | Viewed by 3709

Abstract

With the advent of digital technologies, the computer has become a generalized tool for music production. Music can be seen as a creative form of human–human communication via a computer, and therefore, research on human–computer and computer–human interfaces is very important. This paper, for the Sensors Special Issue on 800 Years of Research at Padova University, presents a review of the research in the field of music technologies at Padova University by the Centro di Sonologia Computazionale (CSC), focusing on scientific, technological and musical aspects of interaction between musician and computer and between computer and audience. We discuss input devices for detecting information from gestures or audio signals and rendering systems for audience and user engagement. Moreover, we discuss a multilevel conceptual framework, which allows multimodal expressive content processing and coordination, which is important in art and music. Several paradigmatic musical works that stated new lines of both musical and scientific research are then presented in detail. The preservation of this heritage presents problems very different from those posed by traditional artworks. CSC is actively engaged in proposing new paradigms for the preservation of digital art. Full article

(This article belongs to the Special Issue 800 Years of Research at Padova University)

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16 pages, 568 KiB

Open AccessArticle

Quantitative Characterization of Motor Control during Gait in Dravet Syndrome Using Wearable Sensors: A Preliminary Study

by Maria Cristina Bisi, Roberto Di Marco, Francesca Ragona, Francesca Darra, Marilena Vecchi, Stefano Masiero, Alessandra Del Felice and Rita Stagni

Sensors 2022, 22(6), 2140; https://doi.org/10.3390/s22062140 - 10 Mar 2022

Cited by 3 | Viewed by 2601

Abstract

Dravet syndrome (DS) is a rare and severe form of genetic epilepsy characterized by cognitive and behavioural impairments and progressive gait deterioration. The characterization of gait parameters in DS needs efficient, non-invasive quantification. The aim of the present study is to apply nonlinear indexes calculated from inertial measurements to describe the dynamics of DS gait. Twenty participants (7 M, age 9–33 years) diagnosed with DS were enrolled. Three wearable inertial measurement units (OPAL, Apdm, Portland, OR, USA; Miniwave, Cometa s.r.l., Italy) were attached to the lower back and ankles and 3D acceleration and angular velocity were acquired while participants walked back and forth along a straight path. Segmental kinematics were acquired by means of stereophotogrammetry (SMART, BTS). Community functioning data were collected using the functional independence measure (FIM). Mean velocity and step width were calculated from stereophotogrammetric data; fundamental frequency, harmonic ratio, recurrence quantification analysis, and multiscale entropy (τ = 1...6) indexes along anteroposterior (AP), mediolateral (ML), and vertical (V) axes were calculated from trunk acceleration. Results were compared to a reference age-matched control group (112 subjects, 6–25 years old). All nonlinear indexes show a disruption of the cyclic pattern of the centre of mass in the sagittal plane, quantitatively supporting the clinical observation of ataxic gait. Indexes in the ML direction were less altered, suggesting the efficacy of the compensatory strategy (widening the base of support). Nonlinear indexes correlated significantly with functional scores (i.e., FIM and speed), confirming their effectiveness in capturing clinically meaningful biomarkers of gait. Full article

(This article belongs to the Special Issue 800 Years of Research at Padova University)

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16 pages, 3747 KiB

Open AccessArticle

Quantitative Evaluation of Hypomimia in Parkinson’s Disease: A Face Tracking Approach

by Elena Pegolo, Daniele Volpe, Alberto Cucca, Lucia Ricciardi and Zimi Sawacha

Sensors 2022, 22(4), 1358; https://doi.org/10.3390/s22041358 - 10 Feb 2022

Cited by 9 | Viewed by 3085

Abstract

Parkinson’s disease (PD) is a neurological disorder that mainly affects the motor system. Among other symptoms, hypomimia is considered one of the clinical hallmarks of the disease. Despite its great impact on patients’ quality of life, it remains still under-investigated. The aim of this work is to provide a quantitative index for hypomimia that can distinguish pathological and healthy subjects and that can be used in the classification of emotions. A face tracking algorithm was implemented based on the Facial Action Coding System. A new easy-to-interpret metric (face mobility index, FMI) was defined considering distances between pairs of geometric features and a classification based on this metric was proposed. Comparison was also provided between healthy controls and PD patients. Results of the study suggest that this index can quantify the degree of impairment in PD and can be used in the classification of emotions. Statistically significant differences were observed for all emotions when distances were taken into account, and for happiness and anger when FMI was considered. The best classification results were obtained with Random Forest and kNN according to the AUC metric. Full article

(This article belongs to the Special Issue 800 Years of Research at Padova University)

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15 pages, 2448 KiB

Open AccessArticle

Opto-Microfluidic Integration of the Bradford Protein Assay in Lithium Niobate Lab-on-a-Chip

by Leonardo Zanini, Annamaria Zaltron, Enrico Turato, Riccardo Zamboni and Cinzia Sada

Sensors 2022, 22(3), 1144; https://doi.org/10.3390/s22031144 - 2 Feb 2022

Cited by 10 | Viewed by 7286

Abstract

This paper deals with the quantification of proteins by implementing the Bradford protein assay method in a portable opto-microfluidic platform for protein concentrations lower than 1.4 mg/mL. Absorbance is measured by way of optical waveguides integrated to a cross-junction microfluidic circuit on a single lithium niobate substrate. A new protocol is proposed to perform the protein quantification based on the high correlation of the light absorbance at 595 nm, as commonly used in the Bradford method, with the one achieved at 633 nm with a cheap commercially available diode laser. This protocol demonstrates the possibility to quantify proteins by using nL volumes, 1000 times less than the standard technique such as paper-analytical devices. Moreover, it shows a limit of quantification of at least 0.12 mg/mL, which is four times lower than the last literature, as well as a better accuracy (98%). The protein quantification is obtained either by using one single microfluidic droplet as well by performing statistical analysis over ensembles of several thousands of droplets in less than 1 min. The proposed methodology presents the further advantage that the protein solutions can be reused for other investigations and the same pertains to the opto-microfluidic platform. Full article

(This article belongs to the Special Issue 800 Years of Research at Padova University)

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19 pages, 4492 KiB

Open AccessArticle

Vibration Energy Harvesting by Means of Piezoelectric Patches: Application to Aircrafts

by Domenico Tommasino, Federico Moro, Bruno Bernay, Thibault De Lumley Woodyear, Enrique de Pablo Corona and Alberto Doria

Sensors 2022, 22(1), 363; https://doi.org/10.3390/s22010363 - 4 Jan 2022

Cited by 16 | Viewed by 3741

Abstract

Vibration energy harvesters in industrial applications usually take the form of cantilever oscillators covered by a layer of piezoelectric material and exploit the resonance phenomenon to improve the generated power. In many aeronautical applications, the installation of cantilever harvesters is not possible owing to the lack of room and/or safety and durability requirements. In these cases, strain piezoelectric harvesters can be adopted, which directly exploit the strain of a vibrating aeronautic component. In this research, a mathematical model of a vibrating slat is developed with the modal superposition approach and is coupled with the model of a piezo-electric patch directly bonded to the slat. The coupled model makes it possible to calculate the power generated by the strain harvester in the presence of the broad-band excitation typical of the aeronautic environment. The optimal position of the piezoelectric patch along the slat length is discussed in relation with the modes of vibration of the slat. Finally, the performance of the strain piezoelectric harvester is compared with the one of a cantilever harvester tuned to the frequency of the most excited slat mode. Full article

(This article belongs to the Special Issue 800 Years of Research at Padova University)

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17 pages, 7338 KiB

Open AccessArticle

Hybrid Sol-Gel Surface-Enhanced Raman Sensor for Xylene Detection in Solution

by Verena Weber, Laura Brigo, Giovanna Brusatin, Giovanni Mattei, Danilo Pedron, Roberto Pilot and Raffaella Signorini

Sensors 2021, 21(23), 7912; https://doi.org/10.3390/s21237912 - 27 Nov 2021

Cited by 2 | Viewed by 2094

Abstract

This paper reports on the fabrication and characterization of a plasmonic/sol-gel sensor for the detection of aromatic molecules. The sol-gel film was engineered using polysilsesquioxanes groups to capture the analyte, through π-π interaction, and to concentrate it close to the plasmonic surface, where Raman amplification occurs. Xylene was chosen as an analyte to test the sensor. It belongs to the general class of volatile organic compounds and can be found in water or in the atmosphere as pollutants released from a variety of processes; its detection with SERS is typically challenging, due to its low affinity toward metallic surfaces. The identification of xylene was verified in comparison with that of other aromatic molecules, such as benzene and toluene. Investigations were carried out on solutions of xylene in cyclohexane, using concentrations in the range from 0 to 800 mM, to evaluate the limit of detection (LOD) of about 40 mM. Full article

(This article belongs to the Special Issue 800 Years of Research at Padova University)

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15 pages, 1147 KiB

Open AccessArticle

Closing the Performance Gap between Siamese Networks for Dissimilarity Image Classification and Convolutional Neural Networks

by Loris Nanni, Giovanni Minchio, Sheryl Brahnam, Davide Sarraggiotto and Alessandra Lumini

Sensors 2021, 21(17), 5809; https://doi.org/10.3390/s21175809 - 29 Aug 2021

Cited by 4 | Viewed by 3252

Abstract

In this paper, we examine two strategies for boosting the performance of ensembles of Siamese networks (SNNs) for image classification using two loss functions (Triplet and Binary Cross Entropy) and two methods for building the dissimilarity spaces (FULLY and DEEPER). With FULLY, the distance between a pattern and a prototype is calculated by comparing two images using the fully connected layer of the Siamese network. With DEEPER, each pattern is described using a deeper layer combined with dimensionality reduction. The basic design of the SNNs takes advantage of supervised k-means clustering for building the dissimilarity spaces that train a set of support vector machines, which are then combined by sum rule for a final decision. The robustness and versatility of this approach are demonstrated on several cross-domain image data sets, including a portrait data set, two bioimage and two animal vocalization data sets. Results show that the strategies employed in this work to increase the performance of dissimilarity image classification using SNN are closing the gap with standalone CNNs. Moreover, when our best system is combined with an ensemble of CNNs, the resulting performance is superior to an ensemble of CNNs, demonstrating that our new strategy is extracting additional information. Full article

(This article belongs to the Special Issue 800 Years of Research at Padova University)

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Review

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35 pages, 4482 KiB

Open AccessReview

Rayleigh-Based Distributed Optical Fiber Sensing

by Luca Palmieri, Luca Schenato, Marco Santagiustina and Andrea Galtarossa

Sensors 2022, 22(18), 6811; https://doi.org/10.3390/s22186811 - 8 Sep 2022

Cited by 60 | Viewed by 7047

Abstract

Distributed optical fiber sensing is a unique technology that offers unprecedented advantages and performance, especially in those experimental fields where requirements such as high spatial resolution, the large spatial extension of the monitored area, and the harshness of the environment limit the applicability of standard sensors. In this paper, we focus on one of the scattering mechanisms, which take place in fibers, upon which distributed sensing may rely, i.e., the Rayleigh scattering. One of the main advantages of Rayleigh scattering is its higher efficiency, which leads to higher SNR in the measurement; this enables measurements on long ranges, higher spatial resolution, and, most importantly, relatively high measurement rates. The first part of the paper describes a comprehensive theoretical model of Rayleigh scattering, accounting for both multimode propagation and double scattering. The second part reviews the main application of this class of sensors. Full article

(This article belongs to the Special Issue 800 Years of Research at Padova University)

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34 pages, 12192 KiB

Open AccessReview

Recent Advancements in Learning Algorithms for Point Clouds: An Updated Overview

by Elena Camuffo, Daniele Mari and Simone Milani

Sensors 2022, 22(4), 1357; https://doi.org/10.3390/s22041357 - 10 Feb 2022

Cited by 39 | Viewed by 8931

Abstract

Recent advancements in self-driving cars, robotics, and remote sensing have widened the range of applications for 3D Point Cloud (PC) data. This data format poses several new issues concerning noise levels, sparsity, and required storage space; as a result, many recent works address PC problems using Deep Learning (DL) solutions thanks to their capability to automatically extract features and achieve high performances. Such evolution has also changed the structure of processing chains and posed new problems to both academic and industrial researchers. The aim of this paper is to provide a comprehensive overview of the latest state-of-the-art DL approaches for the most crucial PC processing operations, i.e., semantic scene understanding, compression, and completion. With respect to the existing reviews, the work proposes a new taxonomical classification of the approaches, taking into account the characteristics of the acquisition set up, the peculiarities of the acquired PC data, the presence of side information (depending on the adopted dataset), the data formatting, and the characteristics of the DL architectures. This organization allows one to better comprehend some final performance comparisons on common test sets and cast a light on the future research trends. Full article

(This article belongs to the Special Issue 800 Years of Research at Padova University)

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